Nesnelerin İnterneti Bağlamında Isıl Konfor Uygulamalarının İncelenmesi

Bu çalışmada Endüstri 4.0 kapsamında, nesnelerin interneti konusunun ısıl konfor alanına getirdiği yenilikler ve klasik yöntemlere göre farklılıkları incelenmiştir. Öncelikle nesnelerin interneti gelecek projeksiyon açısından incelenirken, devamında ısıl konfor uygulamaları hakkında bilgiler verilmiştir. Nesnelerin interneti, mobil ve giyilebilir teknolojiler, çevresel algılayıcılar ile veri toplanması, klasik ısıl konfor ölçekleri ile değerlendirilmesi ve ayrıca kişisel ısıl konfor sistemleri çalışma kapsamında incelenmiştir. Verilerin sınıflandırılması ve yeni modellerin oluşturulması için kullanılan makine öğrenme algoritmalarının işleyişi ve sonuçlar üzerindeki etkisi hakkında klasik ısıl konfor modelleri ile karşılaştırılarak değerlendirilmiştir. Isıl konfor uygulamalarında klasik modellerin belirli bir grup üzerinde sınırlı parametrelerle denenmesi, aynı şartlarda farklı kişiler için farklı sonuçlar vermektedir. Giyilebilir ve mobil teknolojiler kullanılarak elde edilen verilerle, makine öğrenmesi algoritmaları kullanılarak oluşturulan modellerde mevcut popülasyonun tercihleri dikkate alındığından belli periyodlarla güncellenebilmekte ve ısıl konfor açısından memnuniyet klasik modellere göre yüksek ve sürdürülebilir olmaktadır

A Review of Thermal Comfort Applications as a Part of Internet of Things (IoT)

In this study, the innovations and enhancements of thermal comfort which was brought by IoT in scope of Industry 4.0 was introduced and compared with the classical models. Primarily IoT was evaluated in terms of future projection afterwards thermal comfort applications were discussed. IoT, mobile and wearable technologies, data collection with environmental sensors, evaluation of thermal comfort scales and personal thermal comfort systems were also studied within the context of the study. Machine learning algorithms and their effect on the results were evaluated by comparing with the classical thermal comfort models. In thermal comfort applications, testing of classical models with limited parameters on a specific group gives different results for different people under the same conditions. With the data obtained using wearable and mobile technologies, machine learning algorithms can be used to establish a model by considering preferences of current population and they can be updated for certain periods and satisfaction percentage of thermal comfort is high and sustainable in comparison with the classical models.

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